Paper accepted at the NeurIPS'21 workshop: Deep Generative Models and Downstream Applications
All the training logs are in ./tensorboard. The TensorBoard logs can be visualized by running:
tensorboard --logdir=./tensorboard
.
├── LICENSE
├── README.md
├── data # Data directory.
│ ├── cic-ids-2017 # Contains the original datafiles.
│ ├── cic-ids-2017_splits # Contains the train-test split(s) generated by running cic_ids_17_dataset.py.
│ └── cic-ids-2017_splits_with_benign # Contains the train-test split(s) including benign flows generated by running cic_ids_17_dataset.py.
├── models # Directory for saved models. Contains subfolders of structure MODEL_NAME/DATETIME/model-EPOCH.pt
├── tensorboard # Directory for TensorBoard logs. Contains subfolders of structure MODEL_NAME/TIME/logs.
├── cic_ids_17_dataset.py # Contains the data preprocessing pipeline for PyTorch dataset.
├── gans.py # Contains the main experiment class for GAN training.
├── networks.py # Contains the GAN PyTorch modules.
├── train_cgan.py # Script for training conditional GAN.
├── utils.py # Contains utility functions for evaluation and logging.
├── train_classifier.py # Script to train & save a classifier (Random forest) for evaluation of generated flows.
├── data_exploration.ipynb # Jupyter notebook for data exploration steps.
├── train_cgan_colab.ipynb # Jupyter notebook for training cGAN on GPU provided by Google Colab.
└── train_classifier.ipynb # Jupyter notebook for training the classifier used for evaluation of generated flows.